/// \file
/// \ingroup tutorial_roostats
/// \notebook -js
/// Demonstrate Z_Bi = Z_Gamma
///
/// \macro_image
/// \macro_output
/// \macro_code
///
/// \authors Kyle Cranmer, Wouter Verkerke

#include "RooRealVar.h"
#include "RooProdPdf.h"
#include "RooWorkspace.h"
#include "RooDataSet.h"
#include "TCanvas.h"
#include "TH1.h"

using namespace RooFit;
using namespace RooStats;

void Zbi_Zgamma()
{

   // Make model for prototype on/off problem
   // Pois(x | s+b) * Pois(y | tau b )
   // for Z_Gamma, use uniform prior on b.
   RooWorkspace *w1 = new RooWorkspace("w");
   w1->factory("Poisson::px(x[150,0,500],sum::splusb(s[0,0,100],b[100,0,300]))");
   w1->factory("Poisson::py(y[100,0,500],prod::taub(tau[1.],b))");
   w1->factory("Uniform::prior_b(b)");

   // construct the Bayesian-averaged model (eg. a projection pdf)
   // p'(x|s) = \int db p(x|s+b) * [ p(y|b) * prior(b) ]
   w1->factory("PROJ::averagedModel(PROD::foo(px|b,py,prior_b),b)");

   // plot it, blue is averaged model, red is b known exactly
   RooPlot *frame = w1->var("x")->frame();
   w1->pdf("averagedModel")->plotOn(frame);
   w1->pdf("px")->plotOn(frame, LineColor(kRed));
   frame->Draw();

   // compare analytic calculation of Z_Bi
   // with the numerical RooFit implementation of Z_Gamma
   // for an example with x = 150, y = 100

   // numeric RooFit Z_Gamma
   w1->var("y")->setVal(100);
   w1->var("x")->setVal(150);
   RooAbsReal *cdf = w1->pdf("averagedModel")->createCdf(*w1->var("x"));
   cdf->getVal(); // get ugly print messages out of the way

   cout << "Hybrid p-value = " << cdf->getVal() << endl;
   cout << "Z_Gamma Significance  = " << PValueToSignificance(1 - cdf->getVal()) << endl;

   // analytic Z_Bi
   double Z_Bi = NumberCountingUtils::BinomialWithTauObsZ(150, 100, 1);
   std::cout << "Z_Bi significance estimation: " << Z_Bi << std::endl;

   // OUTPUT
   // Hybrid p-value = 0.999058
   // Z_Gamma Significance  = 3.10804
   // Z_Bi significance estimation: 3.10804
}
